Simple CJS model integrated with a growth in weight model to get phi, p, and growth estimates to develop a production model.
In all the models below, 1 = not observed and 2 = observed in the input encounter data.
Encounter data are available here in the eh.csv file. Weight data are in weight.csv
Using model #3 from modelsCMR_Growth_NN_OB.qmd as a staring point for the models, but adapting the model by
Prob not 1) Extending AgeInSamples from 1-11 to 1-x to allow bigger fish to be present for the production estimates. Prob not 2) Loop over first[i]:lastAIS so fish have the chance to survive to large size.
3) Add sample random effect structured by cohort to allow cohort effects on growth.
18.1 By Cohort, modelNum 3: phi(i,t) * g(i,t), p(i,t) model
Model with phi and p for each age-in-samples (t = column in the encounter history file) and individual (i)
Model code is in ./models/production/modelProduction_OB_functionsToSource.R
Model is run ‘by hand’ in ./models/modelProduction_OB_makeFile.R
Functions for this qmd file in ./models/qmdProduction_OB_functionsToSource.R
Model runs:
18.1.1 How many ageInSamples to include?
Code
all <-tar_read(cdWB_electro_target)table(all %>%filter(river =="wb obear")|> dplyr::select(ageInSamples))
library(targets)# Following https://oliviergimenez.github.io/bayesian-cr-workshop/worksheets/4_demo.html# # # To get the mMCMCSummaryRMNA funcion which I adapted to deal with NAssource('./models/production/modelProduction_OB_functionsToSource.R')source('./models/production/qmdProduction_OB_functionsToSource.R')modelNum <-3# phi * growth
18.1.3 Model code
Code
# all cohorts have the same code for a given model. show model code for one of them hereload('./models/production/runsOut/production_OB_model_3_2002__mostRecent.RData')d$modelCode
{
for (i in 1:N) {
weight[i, first[i]] ~ dnorm(meanWeight_AISstd[first[i]],
sd = 2)
weightDATAstd[i, first[i]] ~ dnorm(weight[i, first[i]],
sd = weightSD)
for (t in (first[i]):(last[i] - 1)) {
weight[i, t + 1] <- weight[i, t] + gr[i, t] * sampleIntervalDATA[i,
t]/90
weightDATAstd[i, t + 1] ~ dnorm(weight[i, t + 1],
sd = weightSD)
}
}
for (i in 1:N) {
for (t in first[i]:(last[i] - 1)) {
gr[i, t] ~ dnorm(grIntT[t], sd = 0.5)
}
}
for (t in 1:(T - 1)) {
grIntT[t] ~ dnorm(0, sd = 1000)
}
delta[1] <- 1
delta[2] <- 0
for (i in 1:N) {
for (t in first[i]:(last[i] - 1)) {
gamma[1, 1, t, i] <- phi[i, t]
gamma[1, 2, t, i] <- 1 - phi[i, t]
gamma[2, 1, t, i] <- 0
gamma[2, 2, t, i] <- 1
}
}
for (t in 1:(T - 1)) {
p[t] ~ dunif(0, 1)
omega[1, 1, t] <- 1 - p[t]
omega[1, 2, t] <- p[t]
omega[2, 1, t] <- 1
omega[2, 2, t] <- 0
}
for (i in 1:N) {
for (t in first[i]:(last[i] - 1)) {
logit(phi[i, t]) <- phiInt[i, t] + phiBeta[1, i,
t] * weight[i, t] + phiBeta[2, i, t] * weight[i,
t] * weight[i, t]
}
}
for (i in 1:N) {
for (t in first[i]:(last[i] - 1)) {
phiInt[i, t] ~ dnorm(phiIntT[t], sd = 2)
for (b in 1:2) {
phiBeta[b, i, t] ~ dnorm(phiBetaT[b, t], sd = 1/0.667)
}
}
}
for (t in 1:(T - 1)) {
phiIntT[t] ~ T(dnorm(0, sd = 0.667), -3.5, 3.5)
phiIntTOut[t] <- ilogit(phiIntT[t])
phiBetaT[1, t] ~ dnorm(0, sd = 1/0.667)
phiBetaT[2, t] ~ dnorm(0, sd = 1/0.667)
}
for (i in 1:N) {
for (t in first[i]:(last[i] - 1)) {
weightZ01[i, t] <- weight[i, t] * z[i, t]
}
}
for (i in 1:N) {
z[i, first[i]] ~ dcat(delta[1:2])
for (j in first[i]:(last[i] - 1)) {
z[i, j + 1] ~ dcat(gamma[z[i, j], 1:2, j, i])
yDATA[i, j + 1] ~ dcat(omega[z[i, j + 1], 1:2, j])
}
}
}
#ojs_define(numTags_OJS_mod3 = dim(s2002$eh$tags)[1]) # all cohorts have the same ehojs_define(summary_OB_OJS_all = sAll)#ojs_define(summary_tt_z_OJS = (summary_tt_z_mod3))
Black dots/line is estimated mass and orange dots are observed masses. The green line is the first observation and the red line is the last observation. The number in the upper right corner of each panel is the fish’s cohort.
18.1.5.3 Plot survival
Black dots/line is estimated probability of survival and orange dots are encountered yes (y = 0.8)/no (y = 0). The green line is the first observation and the red line is the last observation. The number in the upper right corner of each panel is the fish’s cohort.
Combine scohort$summaryMod3_tt_growth data into one data frame
Code
sParamsAll0 <-tibble(.rows =0) |>add_column(!!!s2002$summaryMod3_tt_growth[0,])# Fill tibblefor (cohort in cohorts) { summary_obj <-get(paste0("s", cohort))$summaryMod3_tt_growth summary_obj$cohort <- cohort sParamsAll0 <-bind_rows(sParamsAll0, summary_obj)}# remove brackets for filteringsParamsAll0$varNoIndex <- sParamsAll0$var |>str_remove("\\[.*\\]")# for numeric orderingsParamsAll1 <- sParamsAll0 |>filter(varNoIndex !="phiBetaT") |>mutate(varNumeric1 = var |>str_extract("\\[(\\d+)\\]") %>%str_remove_all("\\[|\\]") %>%as.numeric() )sParamsAll2 <- sParamsAll0 |>filter(varNoIndex =="phiBetaT") |>mutate(varNumeric1 = var |>str_extract("\\[(\\d+),") %>%# Changed to match number before commastr_remove_all("\\[|,") %>%# Remove [ and commaas.numeric(),varNumeric2 = var |>str_extract(",\\s*(\\d+)\\]") %>%str_remove_all(",|\\s|\\]") %>%as.numeric() )sParamsAll =bind_rows(sParamsAll1, sParamsAll2)
18.1.6.1 Combined cohorts
18.1.6.2 p
Code
ggplot(sParamsAll |>filter(varNoIndex =="p"), aes(varNumeric1, mean, group = cohort, color = cohort)) +geom_point() +geom_line() +xlab("AgeInSamples")
18.1.6.3 phi
Code
ggplot(sParamsAll |>filter(varNoIndex =="phiIntTOut"), aes(varNumeric1, mean, group = cohort, color = cohort)) +geom_point() +geom_line() +xlab("AgeInSamples")
18.1.6.4 grIntT
Code
ggplot(sParamsAll |>filter(varNoIndex =="grIntT"), aes(varNumeric1, mean, group = cohort, color = cohort)) +geom_point() +geom_line() +xlab("AgeInSamples")
18.1.6.5 grIntT, filtered
Code
ggplot(sParamsAll |>filter(varNoIndex =="grIntT", mean >-5, mean <5), aes(varNumeric1, mean, group = cohort, color = cohort)) +geom_point() +geom_line() +xlab("AgeInSamples")
18.1.6.6 phiBetaT
Code
ggplot(sParamsAll |>filter(varNoIndex =="phiBetaT", mean >-5, mean <5), aes(varNumeric2, mean, group = cohort, color = cohort)) +geom_point() +geom_line() +xlab("AgeInSamples") +facet_wrap((~varNumeric1))